While up to recent years the GTI problem has been focused on learning static networks, i.e., networks which do not change their structure over time, the pervasiveness of networks with a time-varying component has quickly demanded new learning paradigms. This is the case for biological networks [1]}, subject to changes due to genetic and environmental factors, or financial markets [2]}, subject to changes due to political factors, among others. In these scenarios, a static approach would fail in accounting for the temporal variability of the underlying structure, which is strategic to, e.g., detect anomalies or discover new emerging communities.
